Study On Methods Of Optimization Design And Economic Operation Of Drinking Water Distribution Network In Villages And Small Towns

Drinking water distribution network is an important component of infrastructurein small towns and villages, which assumes the important responsibility for supplyingcustomers with safe and reliable water. According to the characteristics of waterdistribution network in small towns and villages, drawing on the experience of themethods of urban water supply, this paper studied on the optimization design ofdrinking water distribution network in small towns and villages, and the methods ofarranging hydraulic monitoring points, the optimal scheduling of pumping station,and the strategy of leakage control.Firstly, the optimization design of drinking water distribution network in smalltowns and villages was studied in detail. The method of “designing flow per second”was used to calculate the pipe flow of tree-structure network. An optimal model wasbuilt to optimize each pipe diameter. The objective function of this model is makingthe cost of construction and operation minimum, while the variable is pipe diameter.The method above was also applied to an actual practice.Secondly, it is based on hydraulic analysis to arrange hydraulic monitoringpoints. For the purposes of optimal operation and accident monitoring, the method of“clustering analysis” was used to arrange pressure monitoring points. The method of“effective detection matrix” was used to layout flow monitoring points.Thirdly, according to the relationship between the power and flow of the pump,an optimal scheduling model of pumping station was built in this paper. The objectivefunction of this model is minimuming the sum of all shaft power, and the variables arethe state of all pumps and adjustable rate. The hybrid algorithm combined immunegenetic algorithm and exterior point method was used to solve the model.Leakage control of water distribution network was studied in the end. When theleakage point produced a certain amount of leakage, the data of pressure monitoringpoints would change accordingly. Built with BP neural network technology, thismodel could find the leakage points according to the rate of change of monitoringpoints data,and an actual instance was used to verify the feasibility of the model.